Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven
talking face generation, the problem of how to efficiently drive the head pose remains …

The objective of this work is to localize sound sources that are visible in a video without
using manual annotations. Our key technical contribution is to show that, by training the …

Generating speech-consistent body and gesture movements is a long-standing problem in
virtual avatar creation. Previous studies often synthesize pose movement in a holistic …

Speech enhancement and speech separation are two related tasks, whose purpose is to
extract either one or more target speech signals, respectively, from a mixture of sounds …

We focus on multi-modal fusion for egocentric action recognition, and propose a novel
architecture for multi-modal temporal-binding, ie the combination of modalities within a …

Our objective is to transform a video into a set of discrete audio-visual objects using self-
supervised learning. To this end, we introduce a model that uses attention to localize and …

Abstract 3D object detection has seen quick progress thanks to advances in deep learning
on point clouds. A few recent works have even shown state-of-the-art performance with just …